Method of automated discovery of new topics

ABSTRACT

The present disclosure relates to a method for performing automated discovery of new topics from unlimited documents related to any subject domain, employing a multi-component extension of Latent Dirichlet Allocation (MC-LDA) topic models, to discover related topics in a corpus. The resulting data may contain millions of term vectors from any subject domain identifying the most distinguished co-occurring topics that users may be interested in, for periodically building new topic ID models using new content, which may be employed to compare one by one with existing model to measure the significance of changes, using term vectors differences with no correlation with a Periodic New Model, for periodic updates of automated discovery of new topics, which may be used to build a new topic ID model in-memory database to allow query-time linking on massive data-set for automated discovery of new topics.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/873,635, entitled “Method of Automated Discovery of New Topics,”filed Oct. 2, 2015, which is a continuation of U.S. patent applicationSer. No. 14/558,076, entitled “Method for Automated Discovery of NewTopics,” filed on Dec. 2, 2014, which is a non-provisional patentapplication that claims the benefit of U.S. Provisional Application No.61/910,763, entitled “Method for Automated Discovery of New Topics,”filed Dec. 2, 2013, each of which are hereby incorporated by referenceherein in their entirety.

This application is related to U.S. application Ser. No. 14/557,794,entitled “Method for Disambiguating Features in Unstructured Text,”filed Dec. 2, 2014; U.S. application Ser. No. 14/558,300, entitled“Event Detection Through Text Analysis Using Trained Event TemplateModels,” filed Dec. 2, 2014; and U.S. application Ser. No. 14/557,906,entitled “Method of Automated Discovery of Topic Relatedness,” filedDec. 2, 2014; each of which are hereby incorporated by reference intheir entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates in general to data storage and morespecifically to a method for performing automated discovery of newtopics in a corpus.

BACKGROUND

As storage and availability of data grows, a large amount of time isspent identifying data relationships for discovery of new topics.Conventionally, the discovery of new topics is oftentimes performedmanually by repetitive work leading to wasting valuable time of users.

Information can have great value. Assembling and maintaining a databaseto store information involves real costs, such as the costs to acquireinformation, the costs associated with physical assets used to house,secure, and make the information available, and labor costs to managethe information.

As computer processors are becoming more powerful, it would beparticularly useful to save the time that an individual conventionallyspends discovering new topics and identifying relationship criteria withexisting models, or between the source and the target.

Oftentimes there are simple transformations, or complex topicidentification across a large corpus of documents from any subjectdomain, requiring a lot of user's time for discovery of relationshipsassociated with existing data.

Thus, there is a need for a simple and flexible method which assistsusers in connection with performing automated discovery of new topics,employing a new topic database for comparison with the existing topicsfor new application environments.

SUMMARY

Embodiments of the present disclosure provide a method for performingautomated discovery of new topics from unlimited documents related toany subject domain, employing a multi-component extension of LatentDirichlet Allocation (MC-LDA) topic models, to discover related topicsin a corpus. The resulting data may contain millions of term vectorsfrom any subject domain identifying the most distinguished co-occurringtopics that users may be interested in, which may be employed to createa Master Topic Model.

In accordance with one aspect of the present disclosure, the method forautomated discovery of new topics may include multiple topicidentification models with different number of term vectors and otherparameters. For example a topic identification model with 64 termvectors may provide a broader topic scope, while models with 256, 1024,or 16K term vectors may provide more specific fine-grained topics.

According to another embodiment, a new data may contain a large numberof entities/topics in a database, which may be used periodically toparse and extract data from topics that users may be interested in. Thismethod may identify term vectors to change detection using term vectordifferences with no correlation in the Master Topic Model to compare andmeasure the significance of these changes, based on establishedthresholds to identify the similarity of the topics found by comparingone by one with topics from Periodic New model.

The present disclosure may provide a method for automated discovery ofnew topics in a corpus, using new content and comparing it to theexisting model for periodically building new topic ID model databasecompressed into the smallest memory footprint possible, for providingfuzzy indexing to allow query-time linking on massive data sets,providing reliability and fault-tolerance through data, which mayprevent software and hardware redundancy.

In one embodiment, a method comprises automatically extracting, by adatabase source computer, from a document corpus, data associated with aplurality of co-occurring topics; in response to automaticallyextracting the plurality of co-occurring topics, extracting, by asynchronizing framework computer, a plurality of topic identifies fromthe plurality of co-occurring topics; creating, by the synchronizingframework computer, a master topic computer model for the documentcorpus from a first plurality of term vectors; creating, by thesynchronizing framework computer, a periodic new topic computer model bycomparing topic significance among the plurality of topic identifiers,the periodic new topic computer model including a second plurality ofterm vectors; and selecting, by the synchronizing framework computer,one or more new topics by identifying one or more term vectors from thesecond plurality of term vectors in the periodic new topic computermodel that have no correlation with the first plurality of term vectorsin the master topic computer model.

In another embodiment, a system comprises a database source computermodule configured to extract data associated with a plurality ofco-occurring topics in a document corpus; and a synchronizing frameworkcomputer module configured to: (a) extract a plurality of topicidentifies from the plurality of co-occurring topics; (b) create amaster topic computer model for the document corpus from a firstplurality of term vectors; (c) create a periodic new topic computermodel by comparing topic significance among the plurality of topicidentifiers, the periodic new topic computer model including a secondplurality of term vectors; and (d) select one or more new topics byidentifying one or more term vectors from the second plurality of termvectors in the periodic new topic computer model that have nocorrelation with the first plurality of term vectors in the master topiccomputer model.

In another embodiment, a non-transitory computer readable medium havingstored thereon computer executable instructions executed by a processorcomprises automatically extracting, by a processor executing a databasesource computer module, from a document corpus data associated with aplurality of co-occurring topics; in response to automaticallyextracting the plurality of co-occurring topics, extracting, by theprocessor executing a synchronizing framework computer module, aplurality of topic identifies from the plurality of co-occurring topics;creating, by the processor executing the synchronizing frameworkcomputer, a master topic computer model for the document corpus from afirst plurality of term vectors; creating, by the processor executingthe synchronizing framework computer, a periodic new topic computermodel by comparing topic significance among the plurality of topicidentifiers, the periodic new topic computer model including a secondplurality of term vectors; and selecting, by the processor executing thesynchronizing framework computer, one or more new topics by identifyingone or more term vectors from the second plurality of term vectors inthe periodic new topic computer model that have no correlation with thefirst plurality of term vectors in the master topic computer model.

Numerous other aspects, features, and benefits of the present disclosuremay be made apparent from the following detailed description takentogether with the drawing features.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure can be better understood by referring to thefollowing figures. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating the principles ofthe disclosure. In the figures, reference numerals designatecorresponding parts throughout the different views.

FIG. 1 is a diagram illustrating a system for automated discovery of newtopics, according to an exemplary embodiment.

FIG. 2 is an exemplary flowchart of a computer executed method forautomated discovery of new topics, according to an exemplary embodiment.

FIG. 3 is a diagram illustrating an embodiment of a directed graphicalrepresentation of a multi-component, conditionally-independent LatentDirichlet Allocation (MC-LDA) topic model executed by one or morespecial purpose computer modules of FIG. 1, according to an exemplaryembodiment.

DETAILED DESCRIPTION

The present disclosure is here described in detail with reference toembodiments illustrated in the drawings, which form a part hereof. Otherembodiments may be used and/or other changes may be made withoutdeparting from the spirit or scope of the present disclosure. Theillustrative embodiments described in the detailed description are notmeant to be limiting of the subject matter presented herein.

GLOSSARY OF TERMS

As used herein, the following terms have the following definitions:

“Parse” refers to analyzing the source code of a computer program tomake sure that it is structurally correct before it is compiled andturned into machine code.

“Term vector” refers to an algebraic model for representing textdocuments (and any objects, in general) as vectors of identifiers, suchas, for example, index terms. It is used in information filtering,information retrieval, indexing, and relevancy rankings.

“Database” refers to any system including any combination of clustersand modules suitable for storing one or more collections and suitable toprocess one or more queries.

“Document” refers to a discrete electronic representation of informationhaving a start and end.

“Multi-Document” refers to a document with its tokens, different typesof named entities, and key phrases organized into separate“bag-of-surface-forms” components.

“Corpus” refers to a collection of one or more documents.

“Feature” refers to any information which is at least partially derivedfrom a document.

“Cluster” refers to a collection of features.

“Memory” refers to any hardware component suitable for storinginformation and retrieving said information at a sufficiently highspeed.

“Module” refers to a computer software and/or hardware componentsuitable for carrying out one or more defined tasks.

“Topic” refers to a set of thematic information which is at leastpartially derived from a corpus.

“Query” refers to a request to retrieve information from one or moresuitable databases.

Description of Exemplary Embodiments

Various aspects of the present disclosure describe a system and methodfor automated discovery of new topics in a corpus based on a concept ofco-occurring topics from different pre-built topic models. Thesedifferent topic models are built with different levels of granularity oftopics, vocabulary and converging parameters, thus providing a verticalhierarchy/scalability over a specific domain of interest. Embodiments ofthe present disclosure extend the conventional LDA topic modeling tosupport multi component LDA, where each component is treated asconditionally-independent, given document topic proportions. Thesecomponents can include features like terms, key phrases, entities,facts, among others. Thus, this approach provides a concept ofhorizontal scalability of the topic models over a specific domain. Thecombination of the vertical vocabulary and horizontal feature selectionin the pre-built topic models, provides varied dimensions ofco-occurring topics, which on appropriate clustering and differentialtraining via MEMDB can produce new topics. These new topics would notexists in the pre-built topic models to begin with, but could bediscovered by running the documents in parallel across all the pre-builttopic models.

Embodiments of the present disclosure describe a computer executedmethod for automated discovery of new topics that may facilitate theautomated determination of relationships of corresponding term vectorsfrom any subject domain identifying the most distinguished co-occurringtopics that users may be interested in, which may be employed to createa Master Topic Model.

According to an embodiment, a term vector component may be a searchcomponent configured to return information about documents. In the termvector space model of information retrieval, the documents are modeledas vectors in a high-dimensional space of millions of terms. The termsare derived from words and phrases in the document, which are weightedby their importance within the document and within the corpus ofdocuments. Each document's vector seeks to represent the document in a“vector space,” allowing comparison with vectors derived from othersources, for example, queries, or other documents. Term vectors may beused as the basis of successful algorithms for document ranking,document filtering, document clustering, and relevance feedback.

The embodiments recite a procedure for automated discovery of new topicsin a corpus based on a concept of co-occurring topics from differentpre-built topic models. These different topic models are built withdifferent levels of granularity of topics, vocabulary, and convergingparameters, thereby providing a vertical hierarchy/scalability over aspecific domain of interest. The embodiments can extend LDA topicmodeling to support multi-component LDA, where each component is treatedas conditionally-independent, given document topic proportions. Thesecomponents can include features, such as terms, key phrases, entities,facts, etc. Thus, this approach can provide a concept of horizontalscalability of the topic models over a specific domain. The combinationof the vertical vocabulary and horizontal feature selection in thepre-built topic models provides varied dimensions of co-occurringtopics, which on appropriate clustering and differential training via anin-memory database can produce new topics. These new topics would notexist in the pre-built topic models, to begin with, but could bediscovered by running the documents in parallel across all the pre-builttopic models.

A System for Automated Discovery of New Topics

FIG. 1 illustrates a simplified block diagram of a system architecture100 configured for automated discovery of new topics, from millions ofdocuments related to any subject domain utilizing a Multi-ComponentLatent Dirichlet Allocation (MC-LDA) topic computer model, or similarsuitable process to discover related topics in a corpus for periodicallybuilding new topic ID models, using new content and comparing it to theexisting model.

In accordance with one aspect of the present disclosure, the system forautomated discovery of new topics may include one or more centralservers having a plurality of special purpose software and hardwarecomputer modules, including a database source module 102 which maycontain a large number of entities/topics that users may be interestedin. The resulting data may contain a large number of term vectors fromany subject domain identifying the most distinguished co-occurringtopics that users may be interested in, which may be employed toimplement a Master Topic Model computer module 104.

Although the system architecture 100 includes a single database sourcemodule 102 and a single destination in-memory database module 112, it isto be understood and appreciated that the novel functionality of asystem and method for automatic discovery of new topics may be employedwith any number of sources and/or destination components, which may beremotely located and accessed.

Embodiments of the present disclosure may be directed to a system andmethod for automated discovery of new topics, which may include multipletopic identification models with different numbers of term vectors andother parameters. For example, a topic identification model with 64 termvectors may provide a broader topic scope, while models with 256, 1024,or 16K term vectors may provide more specific fine-grained topics. Eachtopic or document may be analyzed on co-occurring topics across modelsto discover related topics characterized by a particular set of termvectors, making each individual word exchangeable, having goodprobabilities of generating new term vectors facilitating the automateddiscovery of new topics.

According to principles of the present disclosure, the system and methodfor automated discovery of new topics periodically may use new data inthe database source module 102 to select data of interest or itemfeature.

This information may be used periodically to parse and extract data fromtopics that users may be interested in, to compare all term vectors fromMaster Topic Model module 104 with no correlation with term vectors ofPeriodic New Model module 106 employing a Detector of Term VectorDifferences module 108. The system measures the significances of thechanges by comparing each term vector one by one, selecting the morespecific term vectors that do not correlate or have similarities withMaster Topic Model 104, employing different methods or any suitablemethod existing for this type of comparison.

An embodiment of the present disclosure may include a synchronizationframe work computer module 110 which may be a framework of datacollection interfaces that may communicate with database source computermodule 102 and pull data items that may contain relevant information toa project. Employing this process may generate a new set of topics toproduce from zero to unlimited number of topics, which may be added toMaster Topic Model 104 for periodical updates of automated discovery ofnew topics in a corpus, using the new content and comparing it to theexisting model for periodical building of new topic ID model in-memorydatabase 112. The in-memory database 112 may be compressed into thesmallest memory footprint possible for providing fuzzy indexing to allowquery-time linking on massive data-sets, providing reliability and faulttolerance through data for automated discovery of new topics in acorpus.

The actual software code or specialized control hardware used toimplement these systems, modules and methods are not limiting theinvention. Thus, the operation and behavior of the systems, modules andmethods were described without reference to the specific software code,being understood that software and control hardware may be designed toimplement the systems, modules and methods based on the descriptionherein.

A Method for Automated Discovery of New Topics

FIG. 2 illustrates a flowchart 200 of an embodiment of the methodologyfor automated discovery of new topics in accordance with one aspect ofthe present disclosure. For purposes of simplicity of explanation, oneor more methodologies shown in the form of a flowchart may be describedas a series of steps. It is to be understood and appreciated that thesubject disclosure is not limited by the order of the steps, as somesteps may occur in accordance with the present disclosure or in adifferent order and/or concurrency with other steps shown and describedhere. For example, those skilled in the art may understand andappreciate the methodology which may be represented as a series ofinterrelated states or events, such as in a state diagram. Moreover, notall illustrated acts may be required to implement a methodology inaccordance with the present disclosure.

As may be seen in FIG. 2 the method for automatic discovery of newtopics, may initiate data extraction in step 202, which may beconfigured to allow for custom entity extraction workflows for automateddiscovery of new topics. In an embodiment, a database source module 102may be used to parse and extract data 204 of most distinguishedconcurring topics that a user may be interested in, employing LDA orsimilar suitable method to discover topics in a corpus, which, in step206, may be employed by the synchronizing framework module 110 (FIG. 1)to create a Master Topic Model. Term vectors may be used as the basis ofsuccessful algorithms for document ranking and filtering.

In step 208, the method may periodically run a new set of data to selecttopics of interest from a very large collection of co-occurring entitiesextracted from a document corpus of the targeted domain. This new datamay be analyzed to discover a relationship between data elements. Inaddition, topic identifiers may be extracted to improve precision forcreation of a Periodic New Model, step 210, using a Detector of TermVectors Differences module 108 of the synchronizing framework module 110to compare and measure the significance of topics based on establishedthresholds, for periodically building new topic ID models using newcontent to identify the similarity of topics found. In step 212, termvectors from Periodic New Model having no correlation with term vectorsof Master Topic Model are identified, where all term vectors arecompared one by one with topics from Master Topic Model. In step 214,all differences may change detection of term vector differences.

The next step 216 involves the addition of selected topics to MasterTopic Model, which, in step 218, may be used to periodically build a newtopics ID model to compress data into smallest memory possibleconfigured to fit into in-memory database 112. In embodiments, thein-memory database 112 may have an advanced searching and imbeddedrecord linking capabilities to provide fuzzy indexing, matching andmatch scores and non-exclusionary searching to provide in-databaseanalytics and to allow query-time linking on massive data-set forautomated discovery of new topics.

FIG. 3 illustrates an embodiment of a multi-component,conditionally-independent Latent Dirichlet Allocation (MC-LDA) topicmodel executed by a special purpose computer module, such as TopicModules 104, 106 discussed above in connection with FIG. 1, andinitialized in accordance with the set forth parameters. In theillustrated embodiment, the MC-LDA model computer module provides acomputer executed framework for horizontal scalability to add differentcomponents based on varied features, including entities, facts,key-phrases, and terms.

The foregoing method descriptions and the process flow diagrams areprovided merely as illustrative examples and are not intended to requireor imply that the steps of the various embodiments must be performed inthe order presented. As will be appreciated by one of skill in the artthe steps in the foregoing embodiments may be performed in any order.Words such as “then,” “next,” etc. may not be intended to limit theorder of the steps; these words are simply used to guide the readerthrough the description of the methods. Although process flow diagramsmay describe the operations as a sequential process, many of theoperations can be performed in parallel or concurrently. In addition,the order of the operations may be re-arranged. A process may correspondto a method, a function, a procedure, a subroutine, a subprogram, etc.When a process corresponds to a function, its termination may correspondto a return of the function to the calling function or the mainfunction.

The various illustrative logical blocks, modules, circuits, andalgorithm steps described in connection with the embodiments disclosedherein may be implemented as electronic hardware, computer software, orcombinations of both. To clearly illustrate this interchangeability ofhardware and software, various illustrative components, blocks, modules,circuits, and steps have been described above generally in terms oftheir functionality. Whether such functionality is implemented ashardware or software depends upon the particular application and designconstraints imposed on the overall system. Skilled artisans mayimplement the described functionality in varying ways for eachparticular application, but such implementation decisions should not beinterpreted as causing a departure from the scope of the presentinvention.

Embodiments implemented in computer software may be implemented insoftware, firmware, middleware, microcode, hardware descriptionlanguages, or any combination thereof. A code segment ormachine-executable instructions may represent a procedure, a function, asubprogram, a program, a routine, a subroutine, a module, a softwarepackage, a class, or any combination of instructions, data structures,or program statements. A code segment may be coupled to another codesegment or a hardware circuit by passing and/or receiving information,data, arguments, parameters, or memory contents. Information, arguments,parameters, data, etc. may be passed, forwarded, or transmitted via anysuitable means including memory sharing, message passing, token passing,network transmission, etc.

The actual software code or specialized control hardware used toimplement these systems and methods is not limiting of the invention.Thus, the operation and behavior of the systems and methods weredescribed without reference to the specific software code beingunderstood that software and control hardware can be designed toimplement the systems and methods based on the description herein.

When implemented in software, the functions may be stored as one or moreinstructions or code on a non-transitory computer-readable orprocessor-readable storage medium. The steps of a method or algorithmdisclosed herein may be embodied in a processor-executable softwaremodule which may reside on a computer-readable or processor-readablestorage medium. A non-transitory computer-readable or processor-readablemedia includes both computer storage media and tangible storage mediathat facilitate transfer of a computer program from one place toanother. A non-transitory processor-readable storage media may be anyavailable media that may be accessed by a computer. By way of example,and not limitation, such non-transitory processor-readable media maycomprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage,magnetic disk storage or other magnetic storage devices, or any othertangible storage medium that may be used to store desired program codein the form of instructions or data structures and that may be accessedby a computer or processor. Disk and disc, as used herein, includecompact disc (CD), laser disc, optical disc, digital versatile disc(DVD), floppy disk, and blu-ray disc where disks usually reproduce datamagnetically, while discs reproduce data optically with lasers.Combinations of the above should also be included within the scope ofcomputer-readable media. Additionally, the operations of a method oralgorithm may reside as one or any combination or set of codes and/orinstructions on a non-transitory processor-readable medium and/orcomputer-readable medium, which may be incorporated into a computerprogram product.

The preceding description of the disclosed embodiments is provided toenable any person skilled in the art to make or use the presentinvention. Various modifications to these embodiments will be readilyapparent to those skilled in the art, and the generic principles definedherein may be applied to other embodiments without departing from thespirit or scope of the invention. Thus, the present invention is notintended to be limited to the embodiments shown herein but is to beaccorded the widest scope consistent with the following claims and theprinciples and novel features disclosed.

What is claimed is:
 1. A computer-implemented method comprising: identifying, by a computer, in one or more document corpora of a data source, a topic of interest based upon one or more concurring topics identified in the one or more document corpora; automatically extracting, by the computer, from a document corpus, data associated with a plurality of co-occurring topics based on the topic of interest; in response to automatically extracting the data associated with the plurality of co-occurring topics, extracting, by the computer, a plurality of topic identifiers from the plurality of co-occurring topics; generating, by the computer, a periodic topic model comprising a set of one or more term vectors by comparing topic significance among the plurality of topic identifiers; periodically creating, by the computer, new topic ID models using data content in the periodic topic model by identifying a similarity of topics, wherein the new topic ID models are stored in an in-memory database; and linking, by the computer, data in the in-memory database for automated discovery of new topics.
 2. The method of claim 1, further comprising determining, by the computer, a relationship of corresponding term vectors from the plurality of co-occurring topics, each co-occurring topic of the plurality of co-occurring topics containing one or more term vectors.
 3. The method of claim 2, further comprising generating, by the computer, a master topic computer model comprising a first set of one or more term vectors identified in text of the document corpus upon determining the relationship of the corresponding term vectors from the plurality of co-occurring topics.
 4. The method of claim 3, further comprising selecting, by the computer, one or more new topics by identifying one or more term vectors from the set of the one or more term vectors in the periodic topic computer model that has no correlation with the first set of one or more term vectors in the master topic computer model.
 5. The method of claim 3, further comprising adding, via the computer, one or more new topics to the master topic computer model.
 6. The method of claim 1, wherein comparing the topic significance among the plurality of topic identifiers is based on a predetermined significance threshold.
 7. The method of claim 3, wherein the master topic computer model is a multi-component extension of a Latent Dirichlet Allocation (MC-LDA) topic model.
 8. The method of claim 1, wherein the periodic topic computer model is a multi-component extension of a Latent Dirichlet Allocation (MC-LDA) topic model.
 9. The method of claim 1, wherein the set of the one or more term vectors in the periodic topic computer model corresponds to a second set of the one or more term vectors.
 10. A system comprising: a database source computer module configured to extract data associated with a plurality of co-occurring topics in a document corpus; and one or more computers comprising one or more processors configured to: identify, in the document corpus stored in the database source, an indication of a topic of interest; automatically extract from a document corpus, data associated with a plurality of co-occurring topics based on the topic of interest; extract a plurality of topic identifiers from the plurality of co-occurring topics in response to the extracting of the data associated with the plurality of co-occurring topics; create a periodic topic model comprising a set of one or more term vectors by comparing topic significance among the plurality of topic identifiers; periodically create new topic ID models using data content in the periodic topic model by identifying a similarity of topics, wherein the new topic ID models are stored in an in-memory database; and link data in the in-memory database for automated discovery of new topics.
 11. The system of claim 10, wherein the one or more computers are further configured to determine a relationship of corresponding term vectors from the plurality of co-occurring topics where each co-occurring topic of the plurality of co-occurring topics containing one or more term vectors.
 12. The system of claim 11, wherein the one or more computers are further configured to generate a master topic computer model comprising a first set of one or more term vectors identified in text of the document corpus upon determining the relationship of the corresponding term vectors from the plurality of co-occurring topics.
 13. The system of claim 12, wherein the one or more computers are further configured to select one or more new topics by identifying one or more term vectors from the set of the one or more term vectors in the periodic topic model that has no correlation with the first set of one or more term vectors in the master topic computer model.
 14. The system of claim 12, wherein the one or more computers are further configured to add one or more new topics to the master topic computer model.
 15. The system of claim 10, wherein comparing the topic significance among the plurality of topic identifiers is based on a predetermined significance threshold.
 16. The system of claim 12, wherein the master topic computer model is a multi-component extension of a Latent Dirichlet Allocation (MC-LDA) topic model.
 17. The system of claim 10, wherein the periodic topic computer model is a multi-component extension of a Latent Dirichlet Allocation (MC-LDA) topic model.
 18. The system of claim 10, wherein the set of the one or more term vectors in the periodic topic computer model corresponds to a second set of the one or more term vectors. 